Understanding the Role AI Will Play in Claims and Underwriting
Overall, AI and similar technologies are a boon for the insurance industry. They can increase underwriting accuracy, allowing carriers to offer coverage for risks that were previously thought uninsurable. That’s a good thing for risk managers, who are increasingly seeking coverage for new and evolving risks.
Sixty-five percent of insurance companies say they plan to invest $10 million or more into AI technologies, according to reporting from NASDAQ. These tools are used during every step of the insurance process, from underwriting to claims management.
For risk managers who are on the other side of the claim, it’s important to understand how adjusters are using these tools and what effects they could have on the process.
As these tools become more common in the industry, it is paramount for agents and risk managers to understand how AI works and how it can help carriers assess and provide coverage for increasingly complex risks.
AI in Underwriting
Underwriters once spent hours researching and poring over claims history and spreadsheets to evaluate a potential insured’s exposures. Now, AI and machine learning algorithms are helping them process vast amounts of data and assign more accurate risk scores in a matter of minutes.
Risk managers and brokers might appreciate the ability to get a quick insurance quote — but before they get too excited, they need to make sure they know how these tools are being used during the insurance process. That way, they can argue against any rates or policy terms that don’t make sense to them.
AI has two specific applications for carriers: data mining and modeling. With data mining, AI collects and organizes vast amounts of data, allowing underwriters to make more informed decisions. They can then use that data to model various exposures, which can help them generate a risk score.
Because AI can process vast amounts of data in minutes, underwriters will be able to sort through more metrics from different sources. These tools will give them a more accurate picture of the risks they’re evaluating and can reduce errors. Insurance Thought Leadership reported that AI can reduce the number of submission errors by as much as 29%. For risk managers, that means they’ll get rates that more accurately reflect their exposures.
The process of using AI and machine learning algorithms to assign risk scores and quicken the underwriting process has been common in personal lines for years now. Commercial lines, which tend to work with more complex risks, are just starting to adopt the same technologies.
From Generating Claims Reports to Assessing Damage
On the claims end, carriers are using AI and machine learning algorithms to automate claims processing tasks, generate claims reports, and detect fraud. These uses are already common in other industries, where 56% of companies are using AI in their customer service and 51% use it to automate manual processes, per a Forbes report. Brokers and risk managers need to understand how these tools are being used to level the playing field between insureds and their adjusters.
On the automation end, carriers have found that AI can be used to digitize documents and to determine whether or not an insured has the proper coverage. In workers’ compensation, for instance, AI and machine learning algorithms are being used to digitize doctor’s notes and scan them for any risk factors that could prolong the claims. Adjusters have also used these tools to generate claims reports.
Through the use of AI-powered chatbots, it can also handle basic customer service communications. In an industry burdened by talent shortages, these tools can free up adjusters staff to respond to more complex claims challenges. Risk managers might spend more time communicating with AI during the claims process, or they might want to double-check claims reports for accuracy if an insurer is using AI.
These tools are also useful in detecting fraud. Returning to the workers’ compensation example, the same tools that scan documents and detect any factors, like a preexisting health condition that could negatively impact claims, can be used to scour documents and make note of irregularities that could indicate fraud.
Using AI To Assess and Insure More Complex Risks
Perhaps the biggest advantage it offers for risk managers and brokers is AI’s ability to assess more complex exposures, allowing insurers to feel more comfortable taking on these new risks.
In the future, carriers will likely rely on AI and machine learning systems to create policies for businesses with straightforward exposures. These tools will free up underwriter’s time so that they can focus on analyzing data and assessing exposures for businesses with more complex insurance needs.
In a world where exposures are constantly shifting, AI and machine learning tools will play a critical role in generating risk profiles. Complex technologies, properties in areas where climate exposures are shifting, and many other businesses will benefit because AI and machine learning will help ensure their businesses remain insurable.
As AI tools become increasingly common in the insurance industry, insureds and their brokers need to be familiar with how it’s being used to evaluate data during the underwriting process and how it can help assess claims. Better yet, they should be using AI themselves to analyze the data associated with their insurance program.
LineSlip uses AI to help clients better understand their insurance policy data. Its AI systems can extract policy information and create data-forward dashboards that risk managers and their brokers can use to inform policy decisions. LineSlip’s AI tools can also be used to generate reports, which risk managers and their brokers can use in presentations to carriers during the renewal process.
These tools help companies get the most out of their insurance data. Brokers and risk managers can access the data dashboards generated by LineSlip and use them to track trends or create reports. All of this data can help inform risk management decisions, and it can help brokers make an argument for better coverage and policy language during renewal negotiations with carriers.
